Bridging GRIM, GRIMMER, Umbrella Plots and TIDES
Ian Hussey
26 January, 2025
library(tidyverse)
library(scales)
library(janitor)
#library(rsprite2)
library(scrutiny)
library(tides)
library(purrr)
library(patchwork)
library(knitr)
library(kableExtra)Generate data
Using {scrutiny} + {tides}
# dat_n_11_tides <-
# expand_grid(mean = seq(from = 0.5, to = 7.5, by = 0.01),
# sd = seq(from = 0, to = 4, by = 0.01)) |>
# mutate(n_obs = 11,
# m_prec = 2,
# sd_prec = 2,
# n_items = 1,
# min_val = 1,
# max_val = 7,
# calculate_min_sd = TRUE) |>
# mutate(grim = pmap(list(as.character(mean), n_obs),
# grim)) |>
# mutate(grimmer = pmap(list(as.character(mean), as.character(sd), n_obs),
# grimmer)) |>
# mutate(tides = pmap(list(mean, sd, n_obs, min_val, max_val, n_items, sd_prec,
# calculate_min_sd = TRUE, verbose = FALSE),
# tides_single)) |>
# unnest(grim) |>
# unnest(grimmer) |>
# unnest(tides)
#
# dat_n_100_tides <-
# expand_grid(mean = seq(from = 0.5, to = 7.5, by = 0.01),
# sd = seq(from = 0, to = 4, by = 0.01)) |>
# mutate(n_obs = 100,
# m_prec = 2,
# sd_prec = 2,
# n_items = 1,
# min_val = 1,
# max_val = 7,
# calculate_min_sd = TRUE) |>
# mutate(grim = pmap(list(as.character(mean), n_obs),
# grim)) |>
# mutate(grimmer = pmap(list(as.character(mean), as.character(sd), n_obs),
# grimmer)) |>
# mutate(tides = pmap(list(mean, sd, n_obs, min_val, max_val, n_items, sd_prec,
# calculate_min_sd = TRUE, verbose = FALSE),
# tides_single)) |>
# unnest(grim) |>
# unnest(grimmer) |>
# unnest(tides)
#
# write_rds(dat_n_11_tides, "results_grim_grimmer_tides_n_11_tides.rds")
# write_rds(dat_n_100_tides, "results_grim_grimmer_tides_n_100_tides.rds")
dat_n_11_tides <- read_rds("results_grim_grimmer_tides_n_11_tides.rds")
dat_n_100_tides <- read_rds("results_grim_grimmer_tides_n_100_tides.rds")GRIM vs GRIM+GRIMMER vs GRIM+GRIMMER+Umbrella plots
Plotting function
plot_helper <- function(dat, color_bounds = TRUE){
dat <- dat |>
mutate(label = case_when(mean < 1 | mean > 7 ~ "Impossible value flagged as possible",
sd < 0 | sd > 3.6 ~ "Impossible value flagged as possible", # Croucher (2004) loose heuristic of max SD as 60% of range (ie 6 * .6 = 3.6)
TRUE ~ "Possible value flagged as possible"))
if(color_bounds == FALSE){
# only GRIM-consistent values
p_grim <- dat |>
filter(grim) |>
ggplot(aes(mean, sd)) +
geom_point(shape = 15, size = 0.5, color = "grey35") +
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
scale_color_viridis_d(begin = 0.3, end = 0.7) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM consistent values")
# only GRIM and GRIMMER-consistent values
p_grimmer <- dat |>
filter(grim & grimmer) |>
ggplot(aes(mean, sd)) +
geom_point(shape = 15, size = 0.5, color = "grey35") +
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
scale_color_viridis_d(begin = 0.3, end = 0.7) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM + GRIMMER consistent values")
# only GRIM and GRIMMER and TIDES-consistent values
p_tides <- dat |>
filter(grim & grimmer & tides_consistent) |>
ggplot(aes(mean, sd)) +
geom_point(shape = 15, size = 0.5, color = "grey35") +
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM + GRIMMER + TIDES consistent values")
return(p_grim +
p_grimmer +
p_tides +
plot_layout(ncol = 1))
}
if(color_bounds){
# with coloring of impossible values
# only GRIM-consistent values with colors
p_temp_with_colors <- dat |>
filter(grim) |>
ggplot(aes(mean, sd, color = label)) +
geom_point(shape = 15, size = 0.5) + # "grey20"
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
scale_color_viridis_d(begin = 0.3, end = 0.7) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM consistent values") +
theme(legend.position = "top",
legend.direction = "vertical") +
guides(color = guide_legend(override.aes = list(size = 4, ncol = 1), title = NULL))
# Extract legend grob - magic chatGPT code no idea how it works
legend_grob <- ggplotGrob(p_temp_with_colors)$grobs[[which(sapply(ggplotGrob(p_temp_with_colors)$grobs, function(x) x$name) == "guide-box")]]
# Convert the legend grob into a patchwork-compatible object
legend <- wrap_elements(grid::grobTree(legend_grob))
# grim plot without legend
p_grim_with_colors <- p_temp_with_colors +
theme(legend.position = "none")
# only GRIM and GRIMMER-consistent values with colors
p_grimmer_with_colors <- dat |>
filter(grim & grimmer) |>
ggplot(aes(mean, sd, color = label)) +
geom_point(shape = 15, size = 0.5) +
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
scale_color_viridis_d(begin = 0.3, end = 0.7) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM + GRIMMER consistent values") +
theme(legend.position = "none")
# only GRIM and GRIMMER and TIDES-consistent values with colors
p_tides_with_colors <- dat |>
filter(grim & grimmer & tides_consistent) |>
ggplot(aes(mean, sd, color = label)) +
geom_point(shape = 15, size = 0.5) +
theme_linedraw() +
scale_y_continuous(breaks = scales::breaks_pretty(n = 8),
limit = c(0, 4),
expand = c(0.01, 0.01)) +
scale_x_continuous(breaks = scales::breaks_pretty(n = 7),
limit = c(0.5, 7.5),
expand = c(0.01, 0.01)) +
scale_color_viridis_d(begin = 0.3, end = 0.7, direction = -1) +
ylab("Standard Deviation") +
xlab("Mean") +
ggtitle("GRIM + GRIMMER + TIDES consistent values") +
theme(legend.position = "none")
return(legend +
p_grim_with_colors +
p_grimmer_with_colors +
p_tides_with_colors +
plot_layout(ncol = 1, heights = c(.1, 1, 1, 1)))
}
}
# proportion of possible values, considering only those where mean is within the scale bounds and SD is within Croucher's (2004) loose heuristic of max SD as 60% of range (ie 6 * .6 = 3.6)
table_helper <- function(dat, min, max, heuristic_max_sd){
dat_feasible <- dat |>
filter(mean >= min & mean <= max & sd >= 0 & sd <= heuristic_max_sd)
data.frame(check = c("GRIM", "GRIM+GRIMMER", "GRIM+GRIMMER+TIDES"),
n_total = dat_feasible |>
count(name = "n_total"),
n_possible = c(dat_feasible |>
filter(grim) |>
count() |>
pull(n),
dat_feasible |>
filter(grim, grimmer) |>
count() |>
pull(n),
dat_feasible |>
filter(grim, grimmer, tides_consistent) |>
count() |>
pull(n))) |>
mutate(proportion = janitor::round_half_up(n_possible/n_total, 3))
}Plot N=11
table_helper(dat_n_11_tides, min = 1, max = 7, heuristic_max_sd = 6*.60) |>
kable(align = "r") |>
kable_classic(full_width = FALSE)| check | n_total | n_possible | proportion |
|---|---|---|---|
| GRIM | 216360 | 43560 | 0.201 |
| GRIM+GRIMMER | 216360 | 12676 | 0.059 |
| GRIM+GRIMMER+TIDES | 216360 | 3335 | 0.015 |
